CN111031502A - Wireless sensor network node positioning method based on goblet sea squirt group algorithm - Google Patents
Wireless sensor network node positioning method based on goblet sea squirt group algorithm Download PDFInfo
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Abstract
The invention discloses a wireless sensor network node positioning method based on a goblet sea squirt group algorithm, which comprises the steps of initializing individual nodes of the goblet sea squirt group, judging the positions of the nodes, performing chain search on the surrounding environment by taking the positions of the nodes as food sources, finding other nodes for confirmation, converting the nodes into food source variables for iterative search, finally searching out all the nodes and outputting the nodes and the like. The method has better positioning accuracy and convergence, is superior to the existing traditional algorithm in the aspects of positioning cost and calculation complexity, and is suitable for a wireless sensor network positioning system.
Description
Technical Field
The invention belongs to the technical field of wireless sensor positioning, and particularly relates to a wireless sensor network node positioning method based on a goblet sea squirt group algorithm.
Background
The wireless sensor network is applied to better management and activity detection, so that the operation and application of the wireless network are realized. The wireless sensor network is most important in practical application about the positioning information of the nodes, if the positions of a part of nodes are unknown, the nodes lose timeliness and make decisions in effective time, at present, although the American Global Positioning System (GPS) and the Beidou satellite navigation System (BDS) are perfectly applied to practical life and the technology of the BDS tends to be perfect, the BDS and the BDS are too high in installation cost and large in size and are not suitable for peculiar terrains with blocked signal transmission. Therefore, a node positioning method which has high positioning accuracy and small volume and is suitable for the current wireless sensor network application is urgently needed.
At present, wireless sensor network node positioning methods are divided into two types, one type is positioning based on measuring the distance between nodes, the general method is to set some beacons on the basis of positioning of a global positioning system/Beidou satellite navigation system, use the positioning information and then calculate approximate coordinates of other nodes through known beacon coordinates by a distance measuring mode, wherein the distance measuring mode comprises a trilateration method, a triangulation method and a maximum likelihood estimation method. However, with the expansion of the current application field, the scale of the wireless sensor network is also continuously expanded, and the traditional ranging method has increasingly significant disadvantages of high computational complexity, large positioning error and the like. Another type of wireless sensor network node positioning method is based on a positioning algorithm without ranging, which estimates the position coordinates of a position node according to the connectivity between networks. With the development of science and technology, the requirement on positioning is higher and higher, the positioning is influenced by a heuristic intelligent optimization algorithm, and the goblet sea squirt group algorithm has high accuracy, good convergence and strong stability, and can definitely stand out in the positioning of the wireless sensor network node.
And determining the relevance between the nodes by using a goblet sea squirt group algorithm, and gradually sensing the positions of other nodes to position the nodes by determining part of the nodes as leaders. Due to the association based on the goblet sea squirt group algorithm, the connection between the nodes becomes tighter, and each node information is only influenced by a single node, similar to the packaged operation, and is easier to operate and control.
The traditional wireless sensor network mainly optimizes a target function to calculate unknown node coordinates, and the problem of high calculation complexity exists when solving a nonlinear equation set in the traditional mathematical calculation method, the traditional distance measurement type algorithm cannot meet the current-stage requirement in the current-stage research of the continuous scale enlargement of the wireless sensor network, the algorithm control parameters based on the goblet sea squirt group are few, the wireless sensor network is not found in the positioning, the convergence is good, the positioning precision is higher, the operation is simpler and more convenient, and the traditional positioning algorithm can be replaced.
Disclosure of Invention
The invention aims at the problems of error amplification after multiple iterations, high calculation complexity and the like caused by errors generated by ranging in wireless network node positioning. An improved algorithm (goblet sea squirt group) based on goblet sea squirt individuals is provided, and compared with the existing algorithm in a simulation mode, the algorithm provided by the invention has the advantages that the positioning accuracy and the positioning speed are qualitatively improved.
The technical scheme adopted by the invention is as follows: a wireless network node positioning method based on a goblet sea squirt group algorithm is characterized by comprising the following steps: a wireless sensor network node positioning method based on a goblet sea squirt group algorithm is characterized by comprising the following steps:
step 1: randomly initializing a population in a three-dimensional space, and calculating the upper limit and the lower limit of a network node space search range according to the fact that nodes exist in a certain space under the actual application condition, and setting search boundary parameters;
step 2: selecting an equipmentThe node position of the American Global Positioning System (GPS) or Beidou satellite navigation positioning system is used as food of goblet sea squirt individual group and is marked as F1；
And step 3: inquiring whether a network node exists correspondingly according to the randomly generated coordinate information, and giving corresponding fitness according to the randomly generated coordinate information and the network node related information; the fitness is determined by the error between the actual value of the information transmission of the network node and the predicted value of the information transmission of the randomly generated coordinates, and is given by the formula f (t) ═ t-tFruit of Chinese wolfberryThe smaller the F (t), the better the fitness.
And 4, step 4: selecting the F points as food, namely the F points have the best fitness and sorting according to the obtained fitness;
and 5: according to the fitness sequence obtained in the step 4, selecting the nodes with high fitness as leaders, transmitting the coordinate information of the food nodes back to the control nodes, and selecting the other nodes as followers;
step 6: updating the coordinates of the leader with high fitness;
and 7: updating the coordinates of the followers;
and 8: detecting whether the node search of any goblet ascidian individual exceeds the boundary condition, and bringing the goblet ascidian individual back to the boundary under the condition that the search of any goblet ascidian individual exceeds the boundary space;
and step 9: judging whether the individual fitness of the leader is optimal or not;
if yes, executing step 10;
if not, rotating to execute the step 5;
step 10: the best individual position of the sea squirt is found out and is taken as a food source point variable to be assigned as FiI is 1,2,3 … … n; wherein n represents the individual number of the goblet sea squirt; then updating is carried out;
step 11: judging whether all the nodes are positioned;
if yes, go to step 12;
if not, rotating to execute the step 3;
step 12: all food positions are output as coordinate information of all network nodes.
The invention has the beneficial effects that: in conclusion, the wireless network sensor network node positioning method based on the individual algorithm of the halymenia goblet removes the distance measurement process among nodes in the conventional positioning, and in the calculation process, the complexity O (t (d x n + C x n)) of the algorithm, wherein t represents the iteration times, d is the number (dimension) of variables, n is the number of solutions, and C represents the cost of an objective function, under the condition of large-scale node positioning requirement, the calculation amount by using the algorithm is greatly reduced,
in the application of the algorithm, a randomly distributed method is initialized for searching, the group positions are updated near distribution points, fine adjustment and iteration are continuously performed, the reliability of the random method is improved, the occurrence of local optimization is avoided to a certain extent, if a leader deviates in the searching process, due to the individual biological mechanism of the goblet sea squirts, the subsequent chains can store the past solution, the goblet sea squirts are composed of chains on a control mechanism, the leader can only control adjacent followers, the range deviation cannot be caused, the good accuracy and convergence are achieved, and in order to avoid the algorithm from deteriorating, after partial goblet sea squirt individual nodes are searched for exceeding the space, the nodes are brought back to the space boundary for continuous searching.
In summary, the method of the invention utilizes the chain distribution of individual nodes of the goblet ascidian group and the reasonable distribution of the global search and the local optimal solution of the line by the follower and the leader to make the node positioning more accurate, and reduces the configuration and use of the global positioning system/Beidou satellite navigation system in the positioning cost.
In practical application, the invention reduces the cost investment by reducing the nodes provided with the positioning system.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
For the convenience of those skilled in the art to understand and implement the present invention, the following detailed description of the present invention is made in conjunction with the accompanying drawings and examples, it is to be understood that the sea squirt search algorithm is the prior art, and it should be understood that the implementation examples described herein are only for illustrating and explaining the present invention and are not to be construed as limiting the present invention.
Referring to fig. 1, the method for positioning a wireless sensor network node based on the goblet sea squirt group algorithm provided by the invention comprises the following steps:
step 1: randomly initializing a population in a three-dimensional space, and calculating the upper limit and the lower limit of a network node space search range according to the fact that nodes exist in a certain space under the actual application condition, and setting search boundary parameters;
step 2: selecting a node position equipped with a Global Positioning System (GPS) or a Beidou satellite navigation positioning system (BDS) as food of the individual group of the goblet sea squirts, and recording the node position as F1；
And step 3: inquiring whether a network node exists correspondingly according to the randomly generated coordinate information, and giving corresponding fitness according to the randomly generated coordinate information and the network node related information; the actual value of the information transmission of the fitness network node is determined by the error between the predicted value transmitted by the randomly generated coordinate information and the actual value of the information transmission of the fitness network node, and the error is determined by the formula F (t) ═ t-tFruit of Chinese wolfberryThe smaller the F (t), the better the fitness.
And 4, step 4: selecting the F points as food, namely the F points have the best fitness and sorting according to the obtained fitness;
and 5: according to the fitness sequence obtained in the step 4, selecting the nodes with high fitness as leaders, transmitting the coordinate information of the food nodes back to the control nodes, and selecting the other nodes as followers;
step 6: updating the coordinates of the leader with high fitness;
updating the coordinates of the leader with high fitness according to the following formula;
wherein
Showing the location of the individual in the first goblet of sea squirt, F1Is the current location of the food source, ubjDenotes the upper limit, lbjThe lower bound is represented by the lower bound,
where L is the current iteration and L is the maximum number of iterations; parameter c2And c3Is at [0,1 ]]Are generated uniformly, and they determine, in fact, whether the next position in dimension j should be towards plus or minus infinity and the step size.
And 7: updating the coordinates of the followers;
the follower coordinate updating formula is as follows:
when i is more than or equal to 2,
represents the location of the ith follower goblet or ascidian individual in the jth dimension, t is time, v0Is the initial velocity, v _ final denotes the final velocity x denotes the position of the final velocity, x0Indicating an initial position; calculating formula of acceleration;
since the time in the optimization is iterative, the difference between iterations is equal to 1, and v is taken into account0When 0, the equation is as follows:
And 8: detecting whether the node search of any goblet ascidian individual exceeds the boundary condition, and bringing the goblet ascidian individual back to the boundary under the condition that the search of any goblet ascidian individual exceeds the boundary space;
and step 9: judging whether the individual fitness of the leader is optimal or not;
if yes, executing step 10;
if not, rotating to execute the step 5;
step 10: the best individual position of the sea squirt is found out and is taken as a food source point variable to be assigned as FiI is 1,2,3 … … n; wherein n represents the individual number of the goblet sea squirt; then updating is carried out;
step 11: judging whether all the nodes are positioned;
if yes, go to step 12;
if not, rotating to execute the step 3;
step 12: all food positions are output as coordinate information of all network nodes.
Compared with the three conventional distance measurement algorithms, the method greatly reduces the calculation amount and the cost investment, accelerates the positioning speed and has good convergence, and can be used in the field of node positioning.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A wireless sensor network node positioning method based on a goblet sea squirt group algorithm is characterized by comprising the following steps:
step 1: randomly initializing a population in a three-dimensional space, and calculating the upper limit and the lower limit of a network node space search range according to the fact that nodes exist in a certain space under the actual application condition, and setting search boundary parameters;
step 2: selecting a node position equipped with a Global Positioning System (GPS) or a Beidou satellite navigation positioning system (BDS) as food of the individual group of the goblet sea squirts, and recording the node position as F1；
And step 3: inquiring whether a network node exists correspondingly according to the randomly generated coordinate information, and giving corresponding fitness according to the randomly generated coordinate information and the network node related information;
and 4, step 4: selecting the F points as food, namely the F points have the best fitness and sorting according to the obtained fitness;
and 5: according to the fitness sequence obtained in the step 4, selecting the nodes with high fitness as leaders, transmitting the coordinate information of the food nodes back to the control nodes, and selecting the other nodes as followers;
step 6: updating the coordinates of the leader with high fitness;
and 7: updating the coordinates of the followers;
and 8: detecting whether the node search of any goblet ascidian individual exceeds the boundary condition, and bringing the goblet ascidian individual back to the boundary under the condition that the search of any goblet ascidian individual exceeds the boundary space;
and step 9: judging whether the individual fitness of the leader is optimal or not;
if yes, executing step 10;
if not, rotating to execute the step 5;
step 10: the best individual position of the sea squirt is found out and is taken as a food source point variable to be assigned as FiI is 1,2,3 … … n; wherein n represents the individual number of the goblet sea squirt; then updating is carried out;
step 11: judging whether all the nodes are positioned;
if yes, go to step 12;
if not, rotating to execute the step 3;
step 12: all food positions are output as coordinate information of all network nodes.
2. The method as claimed in claim 1, wherein the wireless sensor network node location method based on the ascidian caspice group algorithm comprises: in step 3, the fitness is determined by the error between the actual value of the information transmission of the network node and the predicted value of the information transmission of the randomly generated coordinates, and the calculation formula is f (t) ═ t-tFruit of Chinese wolfberryThe smaller the F (t), the better the fitness.
3. The method as claimed in claim 1, wherein the wireless sensor network node location method based on the ascidian caspice group algorithm comprises: step 6, updating the coordinates of the leader with high fitness according to the following formula;
wherein
Showing the location of the individual in the first goblet of sea squirt, F1Is the current location of the food source, ubjDenotes the upper limit, lbjThe lower bound is represented by the lower bound,
where L is the current iteration and L is the maximum number of iterations; parameter c2And c3Is at [0,1 ]]Are generated uniformly, and they determine, in fact, whether the next position in dimension j should be towards plus or minus infinity and the step size.
4. The method as claimed in claim 1, wherein the wireless sensor network node location method based on the ascidian caspice group algorithm comprises: in step 7, the follower coordinate updating formula is as follows:
when i is more than or equal to 2,
represents the location of the ith follower goblet or ascidian individual in the jth dimension, t is time, v0Is the initial velocity, v _ final denotes the final velocity x denotes the position of the final velocity, x0Indicating an initial position; calculation formula of acceleration:
since the time in the optimization is iterative, the difference between iterations is equal to 1, and v is taken into account0When 0, the equation is as follows:
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112954763A (en) * | 2021-02-07 | 2021-06-11 | 中山大学 | WSN clustering routing method based on goblet sea squirt algorithm optimization |
| CN113255138A (en) * | 2021-05-31 | 2021-08-13 | 河北工业大学 | Load distribution optimization method for power system |
| CN113255138B (en) * | 2021-05-31 | 2023-05-23 | 河北工业大学 | Load distribution optimization method for power system |
| CN113959448A (en) * | 2021-10-26 | 2022-01-21 | 江苏海洋大学 | Underwater terrain auxiliary navigation method based on improved goblet sea squirt group algorithm |
| CN113959448B (en) * | 2021-10-26 | 2023-08-29 | 江苏海洋大学 | Underwater topography auxiliary navigation method based on improved goblet sea squirt swarm algorithm |
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